Identification of certain histological objects such as lymphocytes, cancer nuclei, and glands, is often one of the pre-requisites to grading or diagnosis of disease in histopathology images. The presence, extent, size, shape and other morphological appearance of these structures are important indicators for presence or severity of disease. Moreover, the number or ratio of specific objects (such as cells or cell nuclei) has diagnostic significance for some cancerous conditions, further motivating the need to accurately identify specific objects. For example, in immunohistochemical (IHC) assessment of estrogen receptor (ER) stained slides, positively and negatively expressed tumor cells need to be identified. The proportion of the ER-positively expressed tumor cells in the tumor cell count is computed as the ER score and used to predict if the patient will likely benefit from endocrine therapy such as tamoxifen. See, e.g., American Cancer Association, “Tumor Markers Fact Sheet,” available at http://www.cancer.gov/about-cancer/diagnosis-staging/diagnosis/tumor-markers-fact-sheet.
Differences in staining protocols impose great challenges for automated nuclei detection and classification. See, e.g., Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., and Yener, B., “Histopathological image analysis: A review,” Biomedical Engineering, IEEE Reviews in 2, pp. 147-171 (2009). Stain variations have been posed mainly as an image preprocessing problem, where global color distribution of the whole image is adjusted to align with a predefined range, or the color histogram landmarks of different stains or tissues are matched to those in a template image. See, e.g., Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., and Yener, B., “Histopathological image analysis: A review,” Biomedical Engineering, IEEE Reviews in 2, pp. 147-171 (2009); see also Bagci, U. and Bai, L., “Registration of standardized histological images in feature space,” in Proc. SPIE Medical Imaging 6914, pp. 69142V-1 (2008); see also Basavanhally, A. and Madabhushi, A., “Em-based segmentation-driven color standardization of digitized histopathology,” in Proc. SPIE Medical Imaging, pp. 86760G-86760G, International Society for Optics and Photonics (2013). Some work shows that color standardization using hue-saturation-density (HSD) model improves color consistency without the need for color deconvolution or tissue segmentation. See, e.g., B. E. Bejnordi, N. Timofeeva, I. Otte-Holler, N. Karssemeijer and J. AWM van der Laak, “Quantitative analysis of stain variability in histology slides and an algorithm for standardization,” in Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 904108 (Mar. 20, 2014); see also Ruifrok, A. C. and Johnston, D. A., “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology/the International Academy of Cytology [and] American, Society of Cytology 23(4), pp. 291-299 (2001); see also Basavanhally, A. and Madabhushi, A., “Em-based segmentation-driven color standardization of digitized histopathology,” in Proc. SPIE Medical Imaging, pp. 86760G-86760G, International Society for Optics and Photonics (2013).
However, color distribution alignment aiming at improving stain appearance consistency is risky when classification needs to be performed among objects having the same stain. These objects can have subtle differences in color, while the prevalence of each object could vary significantly from image to image. Thus, for the same stain, cross-image differences in color distribution could be mainly caused by object prevalence instead of stain variation. Blindly aligning the color distribution can introduce more color confusion between objects to be classified.
A common problem in the automated recognition of objects of a particular object type in a digital image of a biological sample is that various features (like for example object size, stain intensity and others) vary greatly. This variability reduces the accuracy of many object recognition approaches, in particular in case object type identification is based on a feature that follows a Gaussian distribution whereby the expected mean of the distribution is only slightly different for objects of the two different object classes.